Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"

A Flexible Machine Learning-Aware Architecture for Future WLANs

Authors: Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.

Abstract: Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.

Dataset description: This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). 

In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:

1. Identifier of the AP to which the STA has been associated.
2. RSSI obtained from the AP to which the STA has been associated.
3. Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.
4. Load in packets per second (pkt/s) that the STA generates.
5. Percentage of data that the AP is able to serve before the user association is done.
6. Amount of traffic load in pkt/s handled by the AP before the user association is done.
7. Airtime in % that the AP enjoys before the user association is done.
8. Throughput in pkt/s that the STA receives after the user association is done.

The dataset has been generated through random simulations, based on the model provided in https://github.com/toniadame/WiFi_AP_Selection_Framework. More details regarding the dataset generation have been provided in https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans.